TRUEnder commited on
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Push model using huggingface_hub.

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Files changed (2) hide show
  1. README.md +26 -36
  2. config_setfit.json +2 -2
README.md CHANGED
@@ -12,19 +12,18 @@ metrics:
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  - recall
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  - f1
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  widget:
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- - text: menang di 3 pilgub pulau jawa , ppp optimis dilirik jadi cawapres
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- - text: salah satu tempat makan di bandung yang menjadi favorit karena masakan yang
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- enak dan pelayanan yang menyenangkan . suasana tempat makan yang serius dipikirkan
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- sehingga mempunyai cita rasa yang menarik dipadu makanan yang enak membuat betah
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- dan berpikir untuk datang lagi . ini asli rekomendasi banget untuk didatangi dan
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- dicoba masakan nya . bandung memang surga nya makanan dan bebek garang benar-benar
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- gahar .
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- - text: makanan nya lumayan enak , sup iga nya nikmat . anak-anak juga bisa sambil
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- bermain karena ada playground nya .
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- - text: menukarkan 5 poin mendapatkan 1 kupon undian , siapa tahu tahun ini menjadi
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- terberuntungan ku mendapatkan salah satu hadiah nya .
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- - text: kalau bukan karena pak jokowi , indonesia pasti sudah tidak ada bentuk nya
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- , terima kasih , pak .
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  pipeline_tag: text-classification
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  inference: true
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  model-index:
@@ -39,30 +38,21 @@ model-index:
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  split: test
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  metrics:
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  - type: accuracy
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- value: 0.75
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  name: Accuracy
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  - type: precision
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- value: 0.75
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  name: Precision
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  - type: recall
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- value: 0.75
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  name: Recall
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  - type: f1
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- value: 0.75
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  name: F1
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  ---
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  # SetFit with firqaaa/indo-sentence-bert-base
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- ## Author
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-
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- **Kelompok 3 :**
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-
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- - Muhammad Guntur Arfianto (20/459272/PA/19933)
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- - Putri Iqlima Miftahuddini (23/531392/NUGM/01467)
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- - Alan Kurniawan (23/531301/NUGM/01382)
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-
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-
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  This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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  The model has been trained using an efficient few-shot learning technique that involves:
@@ -98,9 +88,9 @@ The model has been trained using an efficient few-shot learning technique that i
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  ## Evaluation
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  ### Metrics
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- | Label | Accuracy | Precision | Recall | F1 |
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- |:--------|:---------|:----------|:-------|:-----|
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- | **all** | 0.75 | 0.75 | 0.75 | 0.75 |
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  ## Uses
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@@ -120,7 +110,7 @@ from setfit import SetFitModel
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-8-shot")
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  # Run inference
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- preds = model("menang di 3 pilgub pulau jawa , ppp optimis dilirik jadi cawapres")
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  ```
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  <!--
@@ -180,12 +170,12 @@ preds = model("menang di 3 pilgub pulau jawa , ppp optimis dilirik jadi cawapres
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:-------:|:------:|:-------------:|:---------------:|
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- | 1.0 | 24 | 0.0498 | 0.1801 |
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- | 2.0 | 48 | 0.0032 | 0.1736 |
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- | 3.0 | 72 | 0.0014 | 0.1703 |
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- | **4.0** | **96** | **0.001** | **0.1696** |
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- | 5.0 | 120 | 0.0009 | 0.1712 |
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- | 6.0 | 144 | 0.0008 | 0.1713 |
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  * The bold row denotes the saved checkpoint.
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  ### Framework Versions
 
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  - recall
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  - f1
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  widget:
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+ - text: halaman 97 - 128 tidak ada , diulang halaman 65 - 96 , pembelian hari minggu
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+ tanggal 24 desember sore sekitar jam 4 pembayaran menggunakan kartu atm bri bersamaan
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+ dengan buku the puppeteer dan sirkus pohon
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+ - text: liverpool sukses di kandang tottenham
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+ - text: hai angga , untuk penerbitan tiket reschedule diharuskan melakukan pembayaran
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+ dulu ya .
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+ - text: sedih kalau umat diprovokasi supaya saling membenci .
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+ - text: berada di lokasi strategis jalan merdeka , berseberangan agak ke samping bandung
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+ indah plaza , tapat sebelah kanan jalan sebelum traffic light , parkir mobil cukup
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+ luas . saus bumbu dan lain-lain disediakan cukup lengkap di lantai bawah . di
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+ lantai atas suasana agak sepi . bakso cukup enak dan terjangkau harga nya tetapi
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+ kuah relatif kurang dan porsi tidak terlalu besar
 
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  pipeline_tag: text-classification
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  inference: true
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  model-index:
 
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  split: test
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  metrics:
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  - type: accuracy
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+ value: 0.7171717171717171
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  name: Accuracy
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  - type: precision
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+ value: 0.7171717171717171
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  name: Precision
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  - type: recall
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+ value: 0.7171717171717171
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  name: Recall
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  - type: f1
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+ value: 0.7171717171717171
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  name: F1
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  ---
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  # SetFit with firqaaa/indo-sentence-bert-base
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  This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
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  The model has been trained using an efficient few-shot learning technique that involves:
 
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  ## Evaluation
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  ### Metrics
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+ | Label | Accuracy | Precision | Recall | F1 |
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+ |:--------|:---------|:----------|:-------|:-------|
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+ | **all** | 0.7172 | 0.7172 | 0.7172 | 0.7172 |
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  ## Uses
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  # Download from the 🤗 Hub
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  model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-8-shot")
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  # Run inference
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+ preds = model("liverpool sukses di kandang tottenham")
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  ```
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  <!--
 
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  ### Training Results
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  | Epoch | Step | Training Loss | Validation Loss |
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  |:-------:|:------:|:-------------:|:---------------:|
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+ | 1.0 | 24 | 0.0498 | 0.2293 |
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+ | 2.0 | 48 | 0.0032 | 0.2033 |
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+ | 3.0 | 72 | 0.0014 | 0.2021 |
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+ | **4.0** | **96** | **0.001** | **0.2009** |
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+ | 5.0 | 120 | 0.0009 | 0.2016 |
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+ | 6.0 | 144 | 0.0008 | 0.2016 |
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  * The bold row denotes the saved checkpoint.
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  ### Framework Versions
config_setfit.json CHANGED
@@ -1,4 +1,4 @@
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  {
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- "labels": null,
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- "normalize_embeddings": false
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  }
 
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  {
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+ "normalize_embeddings": false,
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+ "labels": null
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  }